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DOI 10.1038/s41598-018-20607-7

Медицинская библиотека · Scientific reports · 2018

Early Detection of Apathetic Phenotypes in Huntington's Disease Knock-in Mice Using Open Source Tools.

Minnig Shawn, Bragg Robert M, Tiwana Hardeep S, Solem Wes T, Hovander William S, Vik Eva-Mari S, Hamilton Madeline, Legg Samuel R W, Shuttleworth Dominic D, Coffey Sydney R, Cantle Jeffrey P, Carroll Jeffrey B

СтатьяПоддержка исследования: вне правительства СШАПеревод пока не подготовлен

Аннотация

Apathy is one of the most prevalent and progressive psychiatric symptoms in Huntington's disease (HD) patients. However, preclinical work in HD mouse models tends to focus on molecular and motor, rather than affective, phenotypes. Measuring behavior in mice often produces noisy data and requires large cohorts to detect phenotypic rescue with appropriate power. The operant equipment necessary for measuring affective phenotypes is typically expensive, proprietary to commercial entities, and bulky which can render adequately sized mouse cohorts as cost-prohibitive. Thus, we describe here a home-built, open-source alternative to commercial hardware that is reliable, scalable, and reproducible. Using off-the-shelf hardware, we adapted and built several of the rodent operant buckets (ROBucket) to test HttQ111/+ mice for attention deficits in fixed ratio (FR) and progressive ratio (PR) tasks. We find that, despite normal performance in reward attainment in the FR task, HttQ111/+ mice exhibit reduced PR performance at 9-11 months of age, suggesting motivational deficits. We replicated this in two independent cohorts, demonstrating the reliability and utility of both the apathetic phenotype, and these ROBuckets, for preclinical HD studies.

Для цитирования: Minnig Shawn, Bragg Robert M, Tiwana Hardeep S, Solem Wes T, Hovander William S, Vik Eva-Mari S, Hamilton Madeline, Legg Samuel R W, Shuttleworth Dominic D, Coffey Sydney R, Cantle Jeffrey P, Carroll Jeffrey B. Early Detection of Apathetic Phenotypes in Huntington's Disease Knock-in Mice Using Open Source Tools.. Scientific reports. 2018. DOI: 10.1038/s41598-018-20607-7.

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